Mitsis Giorgos, Tsiropoulou Eirini Eleni, Papavassiliou Symeon
School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athina, Greece.
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Sensors (Basel). 2020 Apr 24;20(8):2434. doi: 10.3390/s20082434.
Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers' computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers' computation resources. In order to properly model the users' risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user's prospect-theoretic utility function is formulated by quantifying the user's risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users' pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user's expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.
无人机辅助的多接入边缘计算（MEC）系统最近已成为一种灵活且动态的计算环境，为用户提供任务卸载服务。为了使这种范式可行，搭载无人机的MEC服务器的运营商应通过向终端用户提供其计算能力来获得某种形式的利润。为了解决本文中的这个问题，我们应用基于使用量的定价策略来允许对服务器计算资源的利用。所提出的定价机制隐含地向用户引入了一种在争夺搭载无人机的MEC服务器计算资源方面更具社会性的行为。为了在整体数据卸载决策过程中正确地对用户的风险感知行为进行建模，采用了前景理论的原则，同时基于公地悲剧理论来考虑对可用计算资源的利用。最初，通过量化用户的风险寻求和损失厌恶行为，同时考虑定价机制，来制定用户的前景理论效用函数。相应地，将用户的定价和风险感知数据卸载问题表述为每个用户预期前景理论效用函数的分布式最大化问题，并作为用户之间的非合作博弈来处理。基于次模博弈理论证明了所制定的非合作博弈存在纯纳什均衡（PNE）。引入了一种迭代分布式算法，该算法遵循最佳响应动态的学习规则收敛到PNE。通过建模和仿真对所提出方法进行性能评估，并给出详细的数值结果以突出其关键操作特征和优势。
